计算机应用与软件2024，Vol.41Issue(1)：56-63,8.DOI:10.3969/j.issn.1000-386x.2024.01.009
基于自适应邻域局部保留ELM-AE的机械故障诊断
MECHANICAL FAULT DIAGNOSIS BASED ON ADAPTIVE NEIGHBORHOOD PRESERVING ELM-AE
摘要
Abstract
In order to solve the problems of prior knowledge dependence and insufficient data mining in machine learning fault diagnosis,a local preserving extreme learning machine automatic encoder based on adaptive neighborhood is proposed.Euclidean distance penalty factor was introduced into the original data space and the embedded representation space for paired samples to realize the similarity classification of data samples.A unified objective function was proposed,which could simultaneously learn data representation and correlation matrix,and a soft discriminative constraint was proposed to prevent overfitting.The experimental results show that the fusion learning association matrix and data representation method has the advantages of fast learning speed,strong generalization ability and high diagnostic accuracy.关键词
极限学习机/自动编码器/关联矩阵学习/自适应邻域/机器故障诊断Key words
Extreme learning machine/Automatic encoder/Affinity learning matrix/Adaptive neighborhood/Machine fault diagnosis分类
信息技术与安全科学引用本文复制引用
张焕可,王帅旗,陈会涛..基于自适应邻域局部保留ELM-AE的机械故障诊断[J].计算机应用与软件,2024,41(1):56-63,8.基金项目
2018年度河南省重点研发与推广专项(182102310793). （182102310793）
